"Trading is statistics and time series analysis." This blog details my progress in developing a systematic trading system for use on the futures and forex markets, with discussion of the various indicators and other inputs used in the creation of the system. Also discussed are some of the issues/problems encountered during this development process. Within the blog posts there are links to other web pages that are/have been useful to me.

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Thursday, 20 March 2014

Update on Recent Work

It has been almost two months since my last post and during this time I have been working on a few different things, all related in one way or another to my desire to create a rolling NN training regime. First off, I have been giving some thought as to the exact methodology to use, and two had come to mind

a rolling look back period of n bars, similar to a moving average

selecting non consecutive periods of price history with similarity to the most recent history

I have not done any work on the first because I feel that it might lead to an unbalanced training set, so I have been working on the second idea. It seemed natural to revisit my earlier work on market classifying with the idea of training a NN for the current market regime using the most recent n bars that have the same market regime classification as the current bar. However, having been somewhat disappointed with the results of my previous work in this area I have been looking at SVM classifiers, in particular the libsvm library. To facilitate this, and following on from my previous post where I mentioned the comp engine timeseries website, I have been hand engineering features for inputs to the SVM. Below is a short video which shows the four features I have come up with. The x and y axis are the same two features in both parts of the video, with the z axis being the third and fourth features. The data are those obtained from my usual idealised market types, with added noise to try and simulate more realistic market conditions. The different colours indicate the five market types that are being modelled.

As can be seen there is nice separation between the market types and the SVM achieves over 98% cross-validation accuracy on this training set. Despite this, when applied to real market data I am yet again disappointed by the performance and choose for now to no longer pursue this avenue of investigation.

Finally, I have also been reassessing the code I use for calculating dominant cycle periods. It is these last two, distance correlation and the period code, that I'm going to look at more fully over the coming days.

The course is at a basic, introductory level and for those areas common to both this course and Andrew Ng's ML course you probably won't learn anything new. However, there is some new stuff, namely Smoothing Splines, Decision Trees, Bootstrapping and Monte Carlo, Linear Discriminant Analysis and Hierarchical Clustering. I also found it useful that all coding is in R rather than Octave.